Abstract Randomized clinical trials (RCTs) show that reducing intraocular pressure (IOP) slows glaucoma progression. Despite the clinician?s use of these RCTs, practice guidelines, and experience, patients still progress to blindness. A gap in clinical science is our lack of knowledge on other risk factors that impact outcomes. Our long-term goal is to improve outcomes by identifying biomarkers, behavioral and environmental factors, that together profile a patient at risk for disease by age-of-onset, rate of progression, poor response to treatment, and large IOP fluctuation. We focus on two IOP patterns that continue to confound the clinician?s ability to provide consistent and effective IOP treatments: (1) IOP response to medications, ranging from non-responder to super responder, and (2) IOP fluctuation, ranging from small to large, with the latter leading to progressive visual field loss. Unfortunately, biomarkers that foretell these IOP patterns, which could improve clinical decision-making have yet to be identified -- a critical barrier to the clinician identifying patients for whom earlier or more aggressive treatment will mitigate glaucoma-related vision loss. The scientific premise is that these mechanisms (i.e., aqueous flow, outflow facility, episcleral venous pressure, and calculated uveoscleral flow) predict a patient?s IOP patterns. We will test the central hypothesis that variations in IOP response to drugs and IOP fluctuation can be predicted by the aqueous humor dynamic (AHD) factors that regulate IOP. We propose to test our hypothesis in 200 patients with ocular hypertension (OHT) or open-angle glaucoma (OAG), as both conditions are investigated in drug trials for IOP drug response. There are two aims: Aim 1. Test the hypothesis that AHD factors predict the IOP drug response. In Protocol 1, AHD factors will be measured under baseline without treatment, and after a randomized order of 1-week treatments with timolol 0.5% followed by a washout period and then latanoprost 0.005% or vice versa. Aim 2. Test the hypothesis that aqueous flow and outflow facility predict IOP fluctuation. In Protocol 2, IOP fluctuation will be measured in the non-clinic setting using the Icare Home tonometer over multiple days at baseline and under monotherapy treatment during Protocol 1. Clinical Impact: Our approach to apply AHD methods to understand variation in drug response and IOP fluctuation is innovative. We predict that AHD factors will explain drug response and IOP fluctuation. Tying-down these relationships will provide new knowledge that will form the basis of future phenotype-genotype studies to identify genetic risk alleles of drug response and IOP fluctuation, resulting in an integrated risk score combining clinical and genetic risk profiles for drug response and IOP fluctuation. The ability to determine which patient needs earlier and more aggressive treatment will ultimately lead to more efficient medical management with fewer follow-up office visits to assess treatment efficacy, fewer treatment failures, and decreased glaucoma-related blindness.